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A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

BIE-ML2.21 Machine Learning 2 Extent of teaching: 2P+2C
Instructor: Vašata D. Completion: Z,ZK
Department: 18105 Credits: 5 Semester: L

Annotation:
The goal of this course is to introduce students to the selected advanced methods of machine learning. In the supervised learning scenario, they, in particular, learn kernel methods and neural networks. In the unsupervised learning scenario students learn the principal component analysis and other dimensionality reduction methods. Moreover, students get the basic principles of reinforcement learning and natural language processing.

Lecture syllabus:
1. Linear basis expansion, Kernel regression
2. Support vector machines for classification
3. Dimensionality reduction - Principal component analysis
4. Dimensionality reduction - Linear discriminant analysis, Locally linear embedding
5. Generative models - Naive Bayes
6. Neural Networks - Perceptron, multi-layer perceptron, deep learning
7. Neural Networks - backpropagation, regularization
8. Neural Networks - convolutional neural networks
9. Neural networks - recurrent neural networks, modern trends
10. Reinforcement learning - introduction, multi-armed bandit
11. Reinforcement learning - Markov decision processes
12. Natural language processing

Seminar syllabus:
1. Linear basis expansion, Kernel regression
2. Support vector machines
3. Dimensionality reduction - Principal component analysis
4. Dimensionality reduction - Linear discriminant analysis, Locally linear embedding
5. Generative models - Naive Bayes
6. Neural Networks - Perceptron, multi-layer perceptron
7. Neural Networks - deep learning, regularization
8. Neural Networks - convolutional neural networks
9. Neural networks - recurrent neural networks
10. Reinforcement learning I 11. Reinforcement learning II
12. Natural language processing

Literature:
1. The Elements of Statistical Learning, Hastie T. and Tibshirani R. and Friedman J., Springer, 2009, ISBN 978-0-387-84857-0
2. Deep Learning, I. Goodfellow, Y. Bengio, A. Courville, MIT Press 2016, ISBN 978-0262035613
3. Reinforcement learning, Sutton, R. S. and Barto, A. G., MIT Press 2018, ISBN 9780262039246

Requirements:
The knowledge of calculus, linear algebra and probability theory is assumed. Furthermore, the knowledge of machine learning corresponding to topics covered in the course BIE-ML1 is also assumed.

All informations and course materials can be fond at https://courses.fit.cvut.cz/BIE-ML2/

The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester
BIE-PS.21 Computer Networks and Internet 2021 PV 6


Page updated 28. 3. 2024, semester: Z/2023-4, L/2019-20, L/2022-3, Z/2019-20, Z/2022-3, L/2020-1, L/2023-4, Z/2020-1, Z,L/2021-2, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška